Enhanced Compressive Threshold Quantum State Tomography for Qudit Systems

• URL: http://arxiv.org/abs/2502.10031v1
• Date: Fri, 14 Feb 2025 09:18:36 GMT
• Title: Enhanced Compressive Threshold Quantum State Tomography for Qudit Systems
• Authors: Giovanni Garberoglio, Maurizio Dapor, Diego Maragnano, Marco Liscidini, Daniele Binosi,
• Abstract summary: We propose an efficient quantum state tomography method inspired by compressed sensing and threshold quantum state tomography. We validate our algorithm with simulations on IBMQ and demonstrate the efficient and accurate reconstruction of $Nleq7$ qubit systems.
• Score: 0.0
• License:
• Abstract: We propose an efficient quantum state tomography method inspired by compressed sensing and threshold quantum state tomography that can drastically reduce the number of measurement settings to reconstruct the density matrix of an $N$-qudit system. We validate our algorithm with simulations on IBMQ and demonstrate the efficient and accurate reconstruction of $N\leq7$ qubit systems, reproducing GHZ, $W$, and random states with $O(1)$, $O(N^2)$, and $O(N)$ settings.

Related papers

• Low-depth quantum symmetrization [1.5566524830295307]
  We present the first quantum algorithm for the general symmetrization problem. Our algorithm, based on (quantum) sorting networks, uses $O(log n)$ depth and $O(nlog m)$ ancilla qubits. We also propose an $O(log2 n)$-depth quantum algorithm to transform second-quantized states to first-quantized states.
  arXiv  Detail & Related papers  (2024-11-06T16:00:46Z)
• Optimizing random local Hamiltonians by dissipation [44.99833362998488]
  We prove that a simplified quantum Gibbs sampling algorithm achieves a $Omega(frac1k)$-fraction approximation of the optimum. Our results suggest that finding low-energy states for sparsified (quasi)local spin and fermionic models is quantumly easy but classically nontrivial.
  arXiv  Detail & Related papers  (2024-11-04T20:21:16Z)
• Efficient Quantum Mixed-State Tomography with Unsupervised Tensor Network Machine Learning [13.02007068572165]
  We propose an efficient mixed-state quantum state scheme based on the locally purified state ansatz. We demonstrate the efficiency and robustness of our scheme on various randomly initiated states with different purities. Our work reveals the prospects of applying network state ansatz and the machine learning approaches for efficient QST of many-body states.
  arXiv  Detail & Related papers  (2023-08-14T02:35:23Z)
• Spacetime-Efficient Low-Depth Quantum State Preparation with Applications [93.56766264306764]
  We show that a novel deterministic method for preparing arbitrary quantum states requires fewer quantum resources than previous methods. We highlight several applications where this ability would be useful, including quantum machine learning, Hamiltonian simulation, and solving linear systems of equations.
  arXiv  Detail & Related papers  (2023-03-03T18:23:20Z)
• Efficient Quantum Simulation of Electron-Phonon Systems by Variational Basis State Encoder [12.497706003633391]
  Digital quantum simulation of electron-phonon systems requires truncating infinite phonon levels into $N$ basis states. We propose a variational basis state encoding algorithm that reduces the scaling of the number of qubits and quantum gates.
  arXiv  Detail & Related papers  (2023-01-04T04:23:53Z)
• Quantum state tomography with tensor train cross approximation [84.59270977313619]
  We show that full quantum state tomography can be performed for such a state with a minimal number of measurement settings. Our method requires exponentially fewer state copies than the best known tomography method for unstructured states and local measurements.
  arXiv  Detail & Related papers  (2022-07-13T17:56:28Z)
• Efficient Bipartite Entanglement Detection Scheme with a Quantum Adversarial Solver [89.80359585967642]
  Proposal reformulates the bipartite entanglement detection as a two-player zero-sum game completed by parameterized quantum circuits. We experimentally implement our protocol on a linear optical network and exhibit its effectiveness to accomplish the bipartite entanglement detection for 5-qubit quantum pure states and 2-qubit quantum mixed states.
  arXiv  Detail & Related papers  (2022-03-15T09:46:45Z)
• Qubit-efficient encoding scheme for quantum simulations of electronic structure [5.16230883032882]
  Simulating electronic structure on a quantum computer requires encoding of fermionic systems onto qubits. We propose a qubit-efficient encoding scheme that requires the qubit number to be only logarithmic in the number of configurations that satisfy the required conditions and symmetries. Our proposed scheme and results show the feasibility of quantum simulations for larger molecular systems in the noisy intermediate-scale quantum (NISQ) era.
  arXiv  Detail & Related papers  (2021-10-08T13:20:18Z)
• A quantum algorithm for training wide and deep classical neural networks [72.2614468437919]
  We show that conditions amenable to classical trainability via gradient descent coincide with those necessary for efficiently solving quantum linear systems. We numerically demonstrate that the MNIST image dataset satisfies such conditions. We provide empirical evidence for $O(log n)$ training of a convolutional neural network with pooling.
  arXiv  Detail & Related papers  (2021-07-19T23:41:03Z)
• Holographic quantum algorithms for simulating correlated spin systems [0.0]
  We present a suite of "holographic" quantum algorithms for efficient ground-state preparation and dynamical evolution of correlated spin-systems. The algorithms exploit the equivalence between matrix-product states (MPS) and quantum channels, along with partial measurement and qubit re-use. As a demonstration of the potential resource savings, we implement a holoVQE simulation of the antiferromagnetic Heisenberg chain on a trapped-ion quantum computer.
  arXiv  Detail & Related papers  (2020-05-06T18:00:01Z)
• Quantum Gram-Schmidt Processes and Their Application to Efficient State Read-out for Quantum Algorithms [87.04438831673063]
  We present an efficient read-out protocol that yields the classical vector form of the generated state. Our protocol suits the case that the output state lies in the row space of the input matrix. One of our technical tools is an efficient quantum algorithm for performing the Gram-Schmidt orthonormal procedure.
  arXiv  Detail & Related papers  (2020-04-14T11:05:26Z)

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.