Resource frugal optimizer for quantum machine learning

• URL: http://arxiv.org/abs/2211.04965v3
• Date: Fri, 28 Jul 2023 13:23:21 GMT
• Title: Resource frugal optimizer for quantum machine learning
• Authors: Charles Moussa, Max Hunter Gordon, Michal Baczyk, M. Cerezo, Lukasz Cincio, Patrick J. Coles
• Abstract summary: Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. We advocate for simultaneous random sampling over both the datasets as well as the measurement operators that define the loss function.
• Score: 0.7046417074932257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. However, training these algorithms can be challenging and calls for tailored optimization procedures. Specifically, QML applications can require a large shot-count overhead due to the large datasets involved. In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function. We consider a highly general loss function that encompasses many QML applications, and we show how to construct an unbiased estimator of its gradient. This allows us to propose a shot-frugal gradient descent optimizer called Refoqus (REsource Frugal Optimizer for QUantum Stochastic gradient descent). Our numerics indicate that Refoqus can save several orders of magnitude in shot cost, even relative to optimizers that sample over measurement operators alone.

Related papers

• Subspace-Based Local Compilation of Variational Quantum Circuits for Large-Scale Quantum Many-Body Simulation [0.0]
  This paper proposes a hybrid quantum-classical algorithm for compiling the time-evolution operator. It achieves a 95% reduction in circuit depth compared to Trotterization while maintaining accuracy. We estimate the gate count needed to execute the quantum simulations using the LSVQC on near-term quantum computing architectures.
  arXiv  Detail & Related papers  (2024-07-19T09:50:01Z)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• Probabilistic Sampling of Balanced K-Means using Adiabatic Quantum Computing [93.83016310295804]
  AQCs allow to implement problems of research interest, which has sparked the development of quantum representations for computer vision tasks. In this work, we explore the potential of using this information for probabilistic balanced k-means clustering. Instead of discarding non-optimal solutions, we propose to use them to compute calibrated posterior probabilities with little additional compute cost. This allows us to identify ambiguous solutions and data points, which we demonstrate on a D-Wave AQC on synthetic tasks and real visual data.
  arXiv  Detail & Related papers  (2023-10-18T17:59:45Z)
• Drastic Circuit Depth Reductions with Preserved Adversarial Robustness by Approximate Encoding for Quantum Machine Learning [0.5181797490530444]
  We implement methods for the efficient preparation of quantum states representing encoded image data using variational, genetic and matrix product state based algorithms. Results show that these methods can approximately prepare states to a level suitable for QML using circuits two orders of magnitude shallower than a standard state preparation implementation.
  arXiv  Detail & Related papers  (2023-09-18T01:49:36Z)
• Exploring the Potential of Qutrits for Quantum Optimization of Graph Coloring [0.0]
  Qutrits may be useful in solving some problems on near-term devices. We run noiseless simulations using PennyLane to compare the formulation against qubit-based QAOA. This work suggests that qutrits may be useful in solving some problems on near-term devices, however further work is required to assess their potential in a noisy environment.
  arXiv  Detail & Related papers  (2023-08-15T21:31:37Z)
• End-to-end resource analysis for quantum interior point methods and portfolio optimization [63.4863637315163]
  We provide a complete quantum circuit-level description of the algorithm from problem input to problem output. We report the number of logical qubits and the quantity/depth of non-Clifford T-gates needed to run the algorithm.
  arXiv  Detail & Related papers  (2022-11-22T18:54:48Z)
• Adiabatic Quantum Computing for Multi Object Tracking [170.8716555363907]
  Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. As these optimization problems are often NP-hard, they can only be solved exactly for small instances on current hardware. We show that our approach is competitive compared with state-of-the-art optimization-based approaches, even when using of-the-shelf integer programming solvers.
  arXiv  Detail & Related papers  (2022-02-17T18:59:20Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Quantum-classical eigensolver using multiscale entanglement renormalization [0.0]
  We propose a variational quantum eigensolver (VQE) for the simulation of strongly-correlated quantum matter. It can have substantially lower costs than corresponding classical algorithms. It is particularly attractive for ion-trap devices with ion-shuttling capabilities.
  arXiv  Detail & Related papers  (2021-08-30T17:46:35Z)
• AQD: Towards Accurate Fully-Quantized Object Detection [94.06347866374927]
  We propose an Accurate Quantized object Detection solution, termed AQD, to get rid of floating-point computation. Our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes.
  arXiv  Detail & Related papers  (2020-07-14T09:07:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.