Measurement-enabled online quantum processing with amplitude encoding
- URL: http://arxiv.org/abs/2606.18991v1
- Date: Wed, 17 Jun 2026 12:12:28 GMT
- Title: Measurement-enabled online quantum processing with amplitude encoding
- Abstract summary: We introduce a quantum reservoir computing online protocol that realizes amplitude encoding on quantum hardware.<n>We present the theoretical formulation of the protocol and a proof-of-principle implementation on quantum hardware.
- Score: 0.5161531917413708
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We introduce a quantum reservoir computing online protocol that realizes amplitude encoding on quantum hardware. Our scheme combines mid-circuit measurement and reset operations to implement the partial-trace dynamics underlying amplitude encoding, while an indirect measurement scheme provides access to reservoir observables without interrupting temporal processing. In contrast to other approaches, our method preserves online operation, avoids input buffering, and keeps the runtime linear in the number of time steps. We present the theoretical formulation of the protocol and a proof-of-principle implementation on quantum hardware, and we evaluate its performance on two standard benchmark tasks. Our results show that the reservoir dynamics can be monitored through both direct measurements of the input qubits and indirect measurements of the memory qubits, enabling observation of the full system while isolating the internal evolution of the reservoir. This work provides a practical route toward scalable hardware implementations of amplitude-encoded quantum reservoir computing and opens the door to systematic experimental studies of complex quantum reservoirs.
Related papers
- Temporal processing of quantum states with hybrid quantum-classical reservoirs [0.5161531917413708]
A quantum-classical hybrid architecture combines a qubit quantum reservoir with a classical echo state network (ESN)<n>We show that the hybrid system outperforms its standalone components in both linear and nonlinear tasks.<n>These results establish hybrid quantum-classical reservoir computing as a practical and scalable route for enhanced quantum machine learning on near-term qubit hardware.
arXiv Detail & Related papers (2026-06-19T11:14:13Z) - Minimal Quantum Reservoirs with Hamiltonian Encoding [72.27323884094953]
We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding.<n>This approach circumvents many of the experimental overheads typically associated with quantum machine learning.
arXiv Detail & Related papers (2025-05-28T16:50:05Z) - Harnessing quantum back-action for time-series processing [0.0]
We demonstrate that incorporating indirect measurements into a quantum machine-learning protocol provides advantages in both execution time scaling and overall performance.<n>Our results reveal that carefully optimizing both the reservoir Hamiltonian parameters and the measurement strength can significantly improve the quantum reservoir computing algorithm performance.
arXiv Detail & Related papers (2024-11-06T15:15:04Z) - Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [62.46800898243033]
Recent progress in quantum learning theory prompts a question: can linear properties of a large-qubit circuit be efficiently learned from measurement data generated by varying classical inputs?<n>We prove that the sample complexity scaling linearly in $d$ is required to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.<n>We propose a kernel-based method leveraging classical shadows and truncated trigonometric expansions, enabling a controllable trade-off between prediction accuracy and computational overhead.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Optimal control in large open quantum systems: the case of transmon readout and reset [44.99833362998488]
We present a framework that combines the adjoint-state method together with reverse-time backpropagation to solve prohibitively large open-system quantum control problems.<n>We apply this framework to optimize two inherently dissipative operations in superconducting qubits.<n>Our results show that while standard pulses for dispersive readout are nearly optimal, adding a transmon drive during the protocol can yield 2x improvements in fidelity and duration.
arXiv Detail & Related papers (2024-03-21T18:12:51Z) - Measurement-induced entanglement and teleportation on a noisy quantum
processor [105.44548669906976]
We investigate measurement-induced quantum information phases on up to 70 superconducting qubits.
We use a duality mapping, to avoid mid-circuit measurement and access different manifestations of the underlying phases.
Our work demonstrates an approach to realize measurement-induced physics at scales that are at the limits of current NISQ processors.
arXiv Detail & Related papers (2023-03-08T18:41:53Z) - Data post-processing for the one-way heterodyne protocol under
composable finite-size security [62.997667081978825]
We study the performance of a practical continuous-variable (CV) quantum key distribution protocol.
We focus on the Gaussian-modulated coherent-state protocol with heterodyne detection in a high signal-to-noise ratio regime.
This allows us to study the performance for practical implementations of the protocol and optimize the parameters connected to the steps above.
arXiv Detail & Related papers (2022-05-20T12:37:09Z) - Time Series Quantum Reservoir Computing with Weak and Projective
Measurements [0.0]
We show that it is possible to exploit the quantumness of the reservoir and to obtain ideal performance.
One consists in rewinding part of the dynamics determined by the fading memory of the reservoir and storing the corresponding data of the input sequence.
The other employs weak measurements operating online at the trade-off where information can be extracted accurately and without hindering the needed memory.
arXiv Detail & Related papers (2022-05-13T17:57:39Z) - Physical reservoir computing using finitely-sampled quantum systems [0.0]
Reservoir computing exploits the nonlinear dynamics of a physical reservoir to perform complex time-series processing tasks.
Here we describe a framework for reservoir computing with nonlinear quantum reservoirs under continuous measurement.
arXiv Detail & Related papers (2021-10-26T16:46:14Z) - Continuous-time dynamics and error scaling of noisy highly-entangling
quantum circuits [58.720142291102135]
We simulate a noisy quantum Fourier transform processor with up to 21 qubits.
We take into account microscopic dissipative processes rather than relying on digital error models.
We show that depending on the dissipative mechanisms at play, the choice of input state has a strong impact on the performance of the quantum algorithm.
arXiv Detail & Related papers (2021-02-08T14:55:44Z) - Reservoir Computing Approach to Quantum State Measurement [0.0]
Reservoir computing is a resource-efficient solution to quantum measurement of superconducting multi-qubit systems.
We show how to operate this device to perform two-qubit state tomography and continuous parity monitoring.
arXiv Detail & Related papers (2020-11-19T04:46:15Z)
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.