Reviewerly/exHarmony huggingface dataset:
QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits

Parameterized Quantum Circuits (PQC) have obtained increasing popularity thanks to their great potential for near-term Noisy Intermediate-Scale Quantum (NISQ) computers. Achieving quantum advantages usually requires a large number of qubits and quantum circuits with enough capacity. However, limited coherence time and massive quantum noises severely constrain the size of quantum circuits that can be executed reliably on real machines. To address these two pain points, we propose QuantumSEA, an in-time sparse exploration for noise-adaptive quantum circuits, aiming to achieve two key objectives: (1) implicit circuits capacity during training - by dynamically exploring the circuit's sparse connectivity and sticking a fixed small number of quantum gates throughout the training which satisfies the coherence time and enjoy light noises, enabling feasible executions on real quantum devices; (2) noise robustness - by jointly optimizing the topology and parameters of quantum circuits under real device noise models. In each update step of sparsity, we leverage the moving average of historical gradients to grow necessary gates and utilize salience-based pruning to eliminate insignificant gates. Extensive experiments are conducted with 7 Quantum Machine Learning (QML) and Variational Quantum Eigensolver (VQE) benchmarks on 6 simulated or real quantum computers, where QuantumSEA consistently surpasses noise-aware search, human-designed, and randomly generated quantum circuit baselines by a clear performance margin. For example, even in the most challenging on-chip training regime, our method establishes state-of-the-art results with only half the number of quantum gates and ~ 2 ×time saving of circuit executions. Codes are available at https://github.com/VITA-Group/QuantumSEA.
Oraqle: Quantum Matter Services for the Masses

Infleqtion's cloud-accessible quantum design platform, “Oraqle,” revolutionizes research and development in quantum technologies. Oraqle enables remote hardware programming, allowing users to dynamically control reconfigurable optical fields applied to ultracold atoms. This “painted potential” capability facilitates the exploration of quantum dynamics and the development of quantum sensing techniques. The platform includes tutorials, demos, and simulations, enabling users to explore potential applications before running them on hardware. We will introduce Oraqle and explore how users can leverage the system for education, quantum discovery, and sensor development.
BBQ-mIS: A Parallel Quantum Algorithm for Graph Coloring Problems

Among the limitations of current quantum machines, the qubits count represents one of the most critical challenges for porting reasonably large computational problems, such as those coming from real-world applications, to the scale of the quantum hardware. In this regard, one possibility is to decompose the problems at hand and exploit parallelism over multiple size-limited quantum resources. To this purpose, we designed a hybrid quantum-classical algorithm, i.e., BBQ-mIS, to solve graph coloring problems on Rydberg atoms quantum machines. The BBQ-mIS algorithm combines the natural representation of Maximum Independent Set (MIS) problems onto the machine Hamiltonian with a Branch&Bound (BB) approach to identify a proper graph coloring. In the proposed solution, the graph representation emerges from qubit interactions (qubits represent vertexes of the graph), and the coloring is then retrieved by iteratively assigning one color to a maximal set of independent vertexes of the graph, still minimizing the number of colors with the Branch&Bound approach. We emulated real quantum hardware onto an IBM Power9-based cluster, with 32 cores/node and 256 GB/node, and exploited an MPI-enhanced library to implement the parallelism for the BBQ-mIS algorithm. Considering this use case, we also identify some technical requirements and challenges for an effective HPC-QC integration. The results show that our problem decomposition is effective in terms of graph coloring solutions quality, and provide a reference for applying this methodology to other quantum technologies or applications.
Noise Robustness of Data Re-Uploading Quantum Classifiers

We investigate the robustness of quantum neural networks (QNNs) trained as binary classifiers against bit flip and depolarizing noise, two channels that emulate a subset of noise present in noisy intermediate-scale quantum (NISQ) devices. The presence of decoherence and other forms of noise poses significant challenges to classification accuracy. To assess the impact of noise on label extraction, and to evaluate the performance and robustness of our models, we trained three different label assignment methods in the presence of various levels of noise. Each QNN model uses 2 qubits to implement a parameterized model and either the qubits of the QNN, or a set of ancilla qubits are used for label extraction. These three different methods project the state of either a single qubit or multiple qubits onto the computational basis. This generates a probability distribution that is post-processed to assign a class label and a class probability. Each QNN is trained using noisy numerical simulation on a dataset with 2 features. Depolarizing noise channels are interleaved with the parameterized gates of the QNN and we include bit flip error prior to extracting the labels. The noise levels for the depolarizing noise and bit flip error are swept over large ranges of values. We deploy QNNs trained at various noise levels on IBM quantum computers- this introduces an additional source of noise due to SWAP gate noise. We observe that the QNNs trained with simulated noise can have good performance on NISQ hardware.
Quantum Error Correction For Dummies

In the current Noisy Intermediate Scale Quantum (NISQ) era of quantum computing, qubit technologies are prone to imperfections, giving rise to various errors such as gate errors, decoherence/dephasing, measurement errors, leakage, and crosstalk. These errors present challenges in achieving error-free computation within NISQ devices. A proposed solution to this issue is Quantum Error Correction (QEC), which aims to rectify the corrupted qubit state through a three-step process: (i) detection: identifying the presence of an error, (ii) decoding: pinpointing the location(s) of the affected qubit(s), and (iii) correction: restoring the faulty qubits to their original states. QEC is an expanding field of research that encompasses intricate concepts. In this paper, we aim to provide a comprehensive review of the historical context, current state, and future prospects of Quantum Error Correction, tailored to cater to computer scientists with limited familiarity with quantum physics and its associated mathematical concepts. In this work, we, (a) explain the foundational principles of QEC and explore existing Quantum Error Correction Codes (QECC) designed to correct errors in qubits, (b) explore the practicality of these QECCs with regard to implementation and error correction quality, and (c) highlight the challenges associated with implementing QEC within the context of the current landscape of NISQ computers.
Efficient and Flexible Annealer-Gate Hybrid Model for Solving Large-Scale Portfolio Optimization

Portfolio optimization is a well-established problem in the field of finance and has recently gained attention in the context of quantum computing. The hardware limitations of quantum computers prevent the direct application of quantum algorithms to large-scale problems. We propose a novel two-stage approach that combines the strengths of both quantum annealing and gate-based quantum computing to efficiently solve large-scale portfolio optimization problems. Building upon the existing Large System Sampling Approximation (LSSA) approach, our work introduces a novel modification to the decomposition and aggregation steps of LSSA. We divide the problem into sub-problems of smaller sizes by solving the Maximum Independent Set (MIS) of the market graph and use a Parameterized Quantum Circuit (PQC) to aggregate the sub-problem solutions. We conduct extensive experiments on real-world data from the US stock market on up to 128 assets on simulators. Our results demonstrate that the proposed approach performs better with the same hardware resources. The outcomes of our research suggest that hybrid annealer-gate quantum computing can provide a practical and scalable solution to large-scale portfolio optimization problems, bridging the gap between theoretical advancements in quantum computing and real-world applications in finance.
Qubit Recycling in Entanglement Distillation

Quantum entanglement distillation is a process to extract a small number of high-fidelity entanglement from a large number of low-fidelity ones, which in essence is to trade yield (or survival rate) for fidelity. Among existing distillation approaches, Gisin's local filtering protocol is commonly adopted in photonic quantum systems for distilling entangled photons in polarization basis. Yet, the performance of Gisin's filter is cursed by the same fundamental trade-off between fidelity and yield. To address this challenge, in this work, we propose a protocol to recycle the disposed photons and improve their fidelity by a designed (and optimized) local operator. The key parameters of the proposed protocol are calculated by solving a constrained optimization problem. In so doing, we achieve significantly higher yield of high-fidelity entanglement pairs. We further evaluate the performance of our designed protocol under two common configurations of Gisin's filter, namely full filter and partial filter. Compared with existing distillation protocols, the results demonstrate that our design achieves as much as 31.2% gain in yield under the same fidelity, while only incurring moderate system complexity in terms of invested hardware and extra signaling for synchronization.
Workshop Summary: Quantum Machine Learning

Quantum computing (QC) has made significant progress in recent years, and scientists are exploring its applications across various fields, including quantum machine learning (QML).
Adaptive mitigation of time-varying quantum noise

Current quantum computers suffer from non-stationary noise channels with high error rates, which undermines their reliability and reproducibility. We propose a Bayesian inference based adaptive algorithm that can learn and mitigate quantum noise in response to changing channel conditions. Our study emphasizes the need for dynamic inference of critical channel parameters to improve program accuracy. We use the Dirichlet distribution to model the stochasticity of the Pauli channel. This allows us to perform Bayesian inference, which can improve the performance of probabilistic error cancellation (PEC) under time-varying noise. Our work demonstrates the importance of characterizing and mitigating temporal variations in quantum noise, which is crucial for developing more accurate and reliable quantum technologies. Our results demonstrate that Bayesian PEC can outperform non-adaptive approaches by a factor of 4.5x when measured using Hellinger distance from the ideal distribution.
Noise-aware Token Swapping for Qubit Routing

In Noisy Intermediate-Scale Quantum (NISQ) era devices, the output fidelity of a quantum circuit, under plausible assumptions, decreases exponentially with its size and depth. As such, optimizing these aspects becomes paramount. This is especially true for sparsely connected physical architectures where added swap gates are essential to meet device connectivity requirements. In this work, we adapt the Approximate Token Swapping (ATS) algorithm, traditionally used for routing qubits, to handle noisy settings by giving priority to specific swap sequences over others. This priority is partially determined by the error rates of the couplings in the qubit connectivity graph. We refer to this algorithm as Priority-ATS (PATS). We provide theoretical justification for PATS's effectiveness over the ATS algorithm and experimentally demonstrate its ability to improve the output state's fidelity. In our simulations, we employ a depolarizing channel to represent two-qubit gate errors, with each neighboring qubit pair having a distinct CNOT gate error rate. By applying realistic error rates, our results prominently highlight an enhanced output fidelity of the quantum circuit when PATS is employed.
MORE: Measurement and Correlation Based Variational Quantum Circuit for Multi-Classification

Quantum computing has shown considerable promise for compute-intensive tasks in recent years. For instance, classification tasks based on quantum neural networks (QNN) have garnered significant interest from researchers and have been evaluated in various scenarios. However, the majority of quantum classifiers are currently limited to binary classification tasks due to either constrained quantum computing resources or the need for intensive classical post-processing. In this paper, we propose an efficient quantum multi-classifier called MORE, which stands for measurement and correlation based variational quantum multi-classifier. MORE adopts the same variational ansatz as binary classifiers while performing multi-classification by fully utilizing the quantum information of a single readout qubit. To extract the complete information from the readout qubit, we select three observables that form the basis of a two-dimensional Hilbert space. We then use the quantum state tomography technique to reconstruct the readout state from the measurement results. Afterward, we explore the correlation between classes to determine the quantum labels for classes using the variational quantum clustering approach. Next, quantum label-based supervised learning is performed to identify the mapping between the input data and their corresponding quantum labels. Finally, the predicted label is determined by its closest quantum label when using the classifier. We implement this approach using the Qiskit Python library and evaluate it through extensive experiments on both noise-free and noisy quantum systems. Our evaluation results demonstrate that MORE, despite using a simple ansatz and limited quantum resources, achieves advanced performance.
Modeling Neuronal Activity with Quantum Generative Adversarial Networks

Understanding the information processing in neuronal networks relies on the development of computational models that accurately reproduce their activity data. Machine learning techniques have shown promising results in generating synthetic neuronal data, but interpretability remains an issue due to a large number of parameters requiring fitting. Quantum machine learning models, particularly quantum generative learning, are emerging as more compact alternatives that offer similar outcomes. This study presents an efficient framework for generating synthetic neuronal data using a Quantum Generative Adversarial Network (QGAN), with a quantum generator and a classical discriminator. We tested the proposed framework for the minimal case of two neurons, considering the case of single time-steps. Preliminary results demonstrate the QGAN's capability to achieve reliable outcomes with a reduced number of trainable parameters, scaling efficiently for increasing neuronal network sizes. The model effectively captures spiking frequencies of real data, although further refinement is required to incorporate temporal correlations for more extended time-steps. Despite certain limitations, this study lays the foundation for future advancements in using quantum adversarial generative networks to model neuronal activity. The promising potential of QGANs in this domain highlights the possibility of gaining valuable insights into the functioning of complex biological systems through quantum-inspired computational methods.
Distribution of entanglement in two-dimensional square grid network

We study entanglement generation in a quantum network where repeater nodes can perform n-qubit Greenberger-Horne-Zeilinger(GHZ) swaps, i.e., projective measurements, to fuse <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> imperfect-Fidelity entangled-state fragments. We show that the distance-independent entanglement distribution rate found previously for this protocol, assuming perfectly-entangled states at the link level, does not survive. This is true also in the modified protocol we study - one that spatially constrains the repeater nodes involved in the swaps. We obtain analytical formulas for a GHZ swap of multiple Werner states, which might be of independent interest. Whether the distance-independent entanglement rate might re-emerge with a spatio-temporally-optimized scheduling of GHZ swaps and multi-site block-distillation codes, remains open.
Quantum topology optimization of ground structures for near-term devices

Product development processes frequently use numerical simulations and mathematical programming techniques. Structural optimization, especially topology optimization, is one of the most promising techniques for generating insightful design choices. Topology optimization for discrete structures, such as truss structures, can be formulated as a combinatorial optimization problem, where the combination of the existence or absence of the material is optimized. In this study, we examine the usage of quantum computers as a potential solution to discrete topology optimization problems. The proposed method consists of two variational quantum algorithms (VQAs); the first solves the state equilibrium equation for all possible structures, and the second amplifies the probability of the optimal structure in quantum superposition. Several experiments, including a real device experiment, show that the proposed method successfully obtained the optimal structure. These findings suggest that quantum computers could be a potential tool for solving topology optimization problems.
Online Detection of Golden Circuit Cutting Points

Quantum circuit cutting has emerged as a promising method for simulating large quantum circuits using a collection of small quantum machines. Running low-qubit circuit “fragments” not only overcomes the size limitation of near-term hardware, but it also increases the fidelity of the simulation. However, reconstructing measurement statistics requires computational resources-both classical and quantum-that grow exponentially with the number of cuts. In this manuscript, we introduce the concept of a golden cutting point, which identifies unnecessary basis components during reconstruction and avoids related downstream computation. We propose a hypothesis-testing scheme for identifying golden cutting points, and provide robustness results in the case of the test failing with low probability. Lastly, we demonstrate the applicability of our method on Qiskit's Aer simulator and observe a reduced wall time from identifying and avoiding obsolete measurements.
Quantum Resource Estimation of Arithmetic Primitives

A major component of RSA cryptanalsysis, factoring a number that is a product of two large primes is computationally intensive. Using Shor's algorithm, there is a possibility of the factorization taking a fraction of the time on a quantum computer. To implement this level of algorithm, an analysis of the resources required would help with understanding the requirements for the hardware based on the software implementation and vice versa.
On the Characterization of Quantum Flip Stars with Quantum Network Tomography

The experimental realization of quantum information systems will be difficult due to how sensitive quantum information is to noise. Overcoming this sensitivity is central to designing quantum networks capable of transmitting quantum information reliably over large distances. Moreover, the ability to characterize communication noise in quantum networks is crucial in developing network protocols capable of overcoming the effects of noise in quantum networks. In this context, quantum network tomography refers to the characterization of channel noise in a quantum network through end-to-end measurements. In this work, we propose network tomography protocols for quantum star networks formed by quantum channels characterized by a single, non-trivial Pauli operator. Our results further the end-to-end characterization of quantum bit-flip star networks by introducing tomography protocols where state distribution and measurements are designed separately. We build upon previously defined quantum network tomography protocols, as well as provide novel methods for the unique characterization of bit-flip probabilities in stars. We introduce a theoretical benchmark based on the Quantum Fisher Information matrix to compare the efficiency of quantum network protocols. We apply our techniques to the protocols proposed, and perform an initial analysis on the potential benefits of entanglement for Quantum Network Tomography. Furthermore, we simulate the protocols using N etSquid to assess the convergence properties of the estimators obtained for particular parameter regimes. Our findings show that the efficiency of protocols depend on parameter values and motivate the search for adaptive quantum network tomography protocols.
QubiC 2.0: A Flexible Advanced Full Stack Quantum Bit Control System

Manipulating qubits and measuring quantum processors via quantum bit control systems is traditionally done using expensive, off-the-shelf commercial electronics setups. Previously, we have introduced QubiC, an open-source FPGA-based quantum bit control system developed at Lawrence Berkeley National Laboratory. After a few years of operating on the QubiC 1.0, we realized the urgent requirements on the mid-circuits measurement and feed forward to implement advanced quantum algorithms. This requirement led to engineering a novel QubiC 2.0 on top of the RFSoC technique, incorporating enriched features. The QubiC 2.0. presented in this manuscript is implemented on the Xilinx evaluation board ZCU216. We developed a portable FPGA gateware with a lean soft processor to handle the quantum commands on the fly. Further, we created a distributed architecture with multiple processors, with a one-to-one mapping between processors and qubits, creating a parallel qubit control and measurement. Like a regular CPU, these processors access pulse envelope commands with parameterized amplitude, phase, and frequency to generate pulse information on the FPGA to ensure the speed and reusability of the entire quantum circuit. The Python software layer abstracts the low-level gateware and provides the compiler, assembler, and remote procedure call for host-to-board communication. The compiled software runs on a Python runtime environment, interfacing with the low-level gateware and hardware connected to a Quantum Processing Unit, creating a full-stack quantum bit control system. We demonstrate the architecture and the broad capabilities of QubiC 2.0 with a simplified feed forward experiment on a conditional circuit.