T-Count Optimizing Genetic Algorithm for Quantum State Preparation

• URL: http://arxiv.org/abs/2406.04004v1
• Date: Thu, 6 Jun 2024 12:26:14 GMT
• Title: T-Count Optimizing Genetic Algorithm for Quantum State Preparation
• Authors: Andrew Wright, Marco Lewis, Paolo Zuliani, Sadegh Soudjani,
• Abstract summary: We present and utilize a genetic algorithm for state preparation circuits consisting of gates from the Clifford + T gate set. Our algorithm does automatically generate fault tolerantly implementable solutions where the number of the most error prone components is reduced.
• Score: 0.05999777817331316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum state preparation is a crucial process within numerous quantum algorithms, and the need for efficient initialization of quantum registers is ever increasing as demand for useful quantum computing grows. The problem arises as the number of qubits to be initialized grows, the circuits required to implement the desired state also exponentially increase in size leading to loss of fidelity to noise. This is mainly due to the susceptibility to environmental effects of the non-Clifford T gate, whose use should thus be reduced as much as possible. In this paper, we present and utilize a genetic algorithm for state preparation circuits consisting of gates from the Clifford + T gate set and optimize them in T-Count as to reduce the impact of noise. Whilst the method presented here does not always produce the most accurate circuits in terms of fidelity, it can generate high-fidelity, non-trivial quantum states such as quantum Fourier transform states. In addition, our algorithm does automatically generate fault tolerantly implementable solutions where the number of the most error prone components is reduced. We present an evaluation of the algorithm when trialed against preparing random, Poisson probability distribution, W, GHZ, and quantum Fourier transform states. We also experimentally demonstrate the scalability issues as qubit count increases, which highlights the need for further optimization of the search process.

Related papers

• Quantum Multiplexer Simplification for State Preparation [0.7270112855088837]
  We propose an algorithm that detects whether a given quantum state can be factored into substates. The simplification is done by eliminating controls of quantum multiplexers. Considering efficiency in terms of depth and number of CNOT gates, our method is competitive with the methods in the literature.
  arXiv  Detail & Related papers  (2024-09-09T13:53:02Z)
• Quantum Circuit Optimization with AlphaTensor [47.9303833600197]
  We develop AlphaTensor-Quantum, a method to minimize the number of T gates that are needed to implement a given circuit. Unlike existing methods for T-count optimization, AlphaTensor-Quantum can incorporate domain-specific knowledge about quantum computation and leverage gadgets. Remarkably, it discovers an efficient algorithm akin to Karatsuba's method for multiplication in finite fields.
  arXiv  Detail & Related papers  (2024-02-22T09:20:54Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Quantum Circuit Optimization through Iteratively Pre-Conditioned Gradient Descent [0.4915744683251151]
  iteratively preconditioned gradient descent (IPG) for optimizing quantum circuits and demonstrate performance speedups for state preparation and implementation of quantum algorithmics. We show an improvement in fidelity by a factor of $104$ for preparing a 4-qubit W state and a maximally entangled 5-qubit GHZ state compared to other commonly used classicals tuning the same ansatz. We also show gains for optimizing a unitary for a quantum Fourier transform using IPG, and report results of running such optimized circuits on IonQ's quantum processing unit (QPU)
  arXiv  Detail & Related papers  (2023-09-18T17:30:03Z)
• Near-Term Distributed Quantum Computation using Mean-Field Corrections and Auxiliary Qubits [77.04894470683776]
  We propose near-term distributed quantum computing that involve limited information transfer and conservative entanglement production. We build upon these concepts to produce an approximate circuit-cutting technique for the fragmented pre-training of variational quantum algorithms.
  arXiv  Detail & Related papers  (2023-09-11T18:00:00Z)
• Challenges of variational quantum optimization with measurement shot noise [0.0]
  We study the scaling of the quantum resources to reach a fixed success probability as the problem size increases. Our results suggest that hybrid quantum-classical algorithms should possibly avoid a brute force classical outer loop.
  arXiv  Detail & Related papers  (2023-07-31T18:01:15Z)
• GASP -- A Genetic Algorithm for State Preparation [0.0]
  We present a genetic algorithm for state preparation (GASP) which generates relatively low-depth quantum circuits for initialising a quantum computer in a specified quantum state. GASP can produce more efficient circuits of a given accuracy with lower depth and gate counts than other methods.
  arXiv  Detail & Related papers  (2023-02-22T04:41:01Z)
• Quantum Speedup for Higher-Order Unconstrained Binary Optimization and MIMO Maximum Likelihood Detection [2.5272389610447856]
  We propose a quantum algorithm that supports a real-valued higher-order unconstrained binary optimization problem. The proposed algorithm is capable of reducing the query complexity in the classical domain and providing a quadratic speedup in the quantum domain.
  arXiv  Detail & Related papers  (2022-05-31T00:14:49Z)
• Simulating the Mott transition on a noisy digital quantum computer via Cartan-based fast-forwarding circuits [62.73367618671969]
  Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model. Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models. This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
  arXiv  Detail & Related papers  (2021-12-10T17:32:15Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)
• Improving the Performance of Deep Quantum Optimization Algorithms with Continuous Gate Sets [47.00474212574662]
  Variational quantum algorithms are believed to be promising for solving computationally hard problems. In this paper, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances. Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.
  arXiv  Detail & Related papers  (2020-05-11T17:20:51Z)

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.