Quantum Computing Techniques for Noise-Resistant Estimation

MIT Sets Quantum Computing Record with 99.998% Fidelity Researchers at MIT have achieved a world-record single-qubit fidelity of 99.998% using a superconducting qubit known as fluxonium. This breakthrough represents a significant step toward practical quantum computing by addressing one of the field’s greatest challenges: mitigating noise and control imperfections that lead to operational errors. Key Highlights: 1. The Problem: Noise and Errors • Qubits, the building blocks of quantum computers, are highly sensitive to noise and imperfections in control mechanisms. • Such disturbances introduce errors that limit the complexity and duration of quantum algorithms. “These errors ultimately cap the performance of quantum systems,” the researchers noted. 2. The Solution: Two New Techniques To overcome these challenges, the MIT team developed two innovative techniques: • Commensurate Pulses: This method involves timing quantum pulses precisely to make counter-rotating errors uniform and correctable. • Circularly Polarized Microwaves: By creating a synthetic version of circularly polarized light, the team improved the control of the qubit’s state, further enhancing fidelity. “Getting rid of these errors was a fun challenge for us,” said David Rower, PhD ’24, one of the study’s lead researchers. 3. Fluxonium Qubits and Their Potential • Fluxonium qubits are superconducting circuits with unique properties that make them more resistant to environmental noise compared to traditional qubits. • By applying the new error-mitigation techniques, the team unlocked the potential of fluxonium to operate at near-perfect fidelity. 4. Implications for Quantum Computing • Achieving 99.998% fidelity significantly reduces errors in quantum operations, paving the way for more complex and reliable quantum algorithms. • This milestone represents a major step toward scalable quantum computing systems capable of solving real-world problems. What’s Next? The team plans to expand its work by exploring multi-qubit systems and integrating the error-mitigation techniques into larger quantum architectures. Such advancements could accelerate progress toward error-corrected, fault-tolerant quantum computers. Conclusion: A Leap Toward Practical Quantum Systems MIT’s achievement underscores the importance of innovation in error correction and control to overcome the fundamental challenges of quantum computing. This breakthrough brings us closer to the realization of large-scale quantum systems that could transform fields such as cryptography, materials science, and complex optimization problems.

❓ Ever wondered how Neural Networks (NNs) could revolutionize #quantum research? #NeuralNetworks aren't just transforming #AI —they're also pivotal in the quantum realm! In the work entitled "Parameter Estimation by Learning Quantum Correlations in Continuous Photon-Counting Data Using Neural Networks." Quantinuum proudly collaborated with global partners, such as the Universidad Autónoma de Madrid, Chalmers University of Technology, and the University of Michigan, uniting expertise from every corner of the world. 🌍 https://lnkd.in/gj8qttdN 🔍 Key Findings: 1️⃣ The study introduces a novel inference method employing artificial neural networks for quantum probe parameter estimation. 2️⃣ This method leverages quantum correlations in discrete photon-counting data, offering a fresh perspective compared to existing techniques focusing on diffusive signals. 3️⃣ The approach achieves performance on par with Bayesian inference - renowned for its optimal information retrieval capability - yet does so at a fraction of the computational cost. 4️⃣ Beyond efficiency, the method stands robust against imperfections in measurement and training data. 5️⃣ Potential applications span from quantum sensing and imaging to precise calibration tasks in laboratory setups. 🤔 Curious About the Unknowns? The authors are sharing EVERYTHING on Zenodo! 🎉 The codes used to generate these results, including the proposed NN architectures as TensorFlow models, are available here https://lnkd.in/gVdzJycM as well as all the data necessary to reproduce the results openly available here: https://lnkd.in/gVdzJycM Enrico Rinaldi, Manuel González Lastre, Sergio Garcia Herreros, Shahnawaz Ahmed, Maryam Khanahmadi, Franco Nori, and Carlos Sánchez Muñoz

Recently the team published a paper in Nature Computational Science in collaboration with researchers from Los Alamos National Lab and the University of Basel. The paper was on provable bounds for noise-free expectation values computed from noisy samples. This calibration started in the optimization working group. The paper discusses how the “Layer Fidelity” or how effective two qubit error as measured by the “Error Per Layered Gate” can be used to quantify the impact of hardware noise on sampling-based quantum (optimization) algorithms. Each one of our devices reports this number in the resource tab of the IBM Quantum Platform (https://lnkd.in/eRd2yKwB). The paper allows you to estimate the number of additional shots required to compensate for the impact of noise. It turns out that by using this method it is much cheaper than mitigating the noise when requiring unbiased estimators of expectation values (sqrt(gamma) vs gamma^2). These insights allowed us to prove that the Conditional Value at Risk (CvaR) – an alternative loss function suggested in 2019 and widely used to train variational algorithms, borrowed from mathematical finance – leads to provable bounds on expectation values using only noisy samples. The theoretical insights have been demonstrated on two use cases using up to 127 qubits: estimation of state fidelity (as required, e.g. to evaluate quantum kernels) and optimization (QAOA). In both cases, the team see a good agreement between the theory and experiment. Read the paper here https://lnkd.in/ehyz4GCJ

Interesting new study: "EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data." The authors introduce a novel framework to address the limitations of traditional amplitude embedding (AE) [GitHub repo included]. Traditional AE methods often involve deep, variable-length circuits, which can lead to high output error due to extensive gate usage and inconsistent error rates across different data samples. This variability in circuit depth and gate composition results in unequal noise exposure, obscuring the true performance of quantum algorithms. To overcome these challenges, the researchers developed EnQode, a fast AE technique based on symbolic representation. Instead of aiming for exact amplitude representation for each sample, EnQode employs a cluster-based approach to achieve approximate AE with high fidelity. Here are some of the key aspects of EnQode: * Clustering: EnQode begins by using the k-means clustering algorithm to group similar data samples. For each cluster, a mean state is calculated to represent the central characteristics of the data distribution within that cluster. * Hardware-optimized ansatz: For each cluster's mean state, a low-depth, machine-optimized ansatz is trained, tailored to the specific quantum hardware being used (e.g., IBM quantum devices). * Transfer Learning for fast embedding: Once the cluster models are trained offline, transfer learning is used for rapid amplitude embedding of new data samples. An incoming sample is assigned to the nearest cluster, and its embedding circuit is initialized with the optimized parameters of that cluster's mean state. These parameters can then be fine-tuned, significantly accelerating the embedding process without retraining from scratch. * Reduced circuit complexity: EnQode achieved an average reduction of over 28× in circuit depth, over 11× in single-qubit gate count, and over 12× in two-qubit gate count, with zero variability across samples due to its fixed ansatz design. * Higher state fidelity in noisy environments: In noisy IBM quantum hardware simulations, EnQode showed a state fidelity improvement of over 14× compared to the baseline, highlighting its robustness to hardware noise. While the baseline achieved 100% fidelity in ideal simulations (as it performs exact embedding), EnQode maintained an average of 89% fidelity when transpiled to real hardware in ideal simulations, which is considered a good approximation given the significant reduction in circuit complexity. Here the article: https://lnkd.in/dQMbNN7b And here the GitHub repo: https://lnkd.in/dbm7q3eJ #qml #datascience #machinelearning #quantum #nisq #quantumcomputing

I'm excited to share our latest work, Demonstration of robust and efficient quantum property learning with shallow shadows, published in Nature Communications! 🎉 📝 Authors: Hong-Ye Hu, Andi Gu, Swarnadeep Majumder, Hang Ren, Yipei Zhang, Derek S. Wang, Yi-Zhuang You, Zlatko Minev, Susanne F. Yelin, Alireza Seif 🔍 Context: Extracting information efficiently from quantum systems is crucial for advancing quantum information processing. Classical shadow tomography offers a powerful technique, but it struggles with noisy, high-dimensional quantum states and complex observables. 🤔 Key Question: Can we overcome noise limitations and improve sample efficiency in quantum state learning, especially for high-weight and non-local observables, using shallow quantum circuits? 💡 Our Findings: We introduce robust shallow shadows—a protocol designed to mitigate noise using Bayesian inference, enabling highly efficient learning of quantum state properties, even in the presence of noise. Our experiments on a 127-qubit superconducting quantum processor confirm the protocol’s practical use, showing up to 5x reduction in sample complexity compared to traditional methods. ✨ Key Takeaways: 1. Noise-resilience: Accurate predictions across diverse quantum state properties. 2. Sample Efficiency: Substantial reduction in sample complexity for high-weight and non-local observables. 3. Scalability: The protocol is well-suited for near-term quantum devices, even with noise. Paper: https://lnkd.in/dW4NJ23Q

> Sharing resource < Interesting paper this morning: "Scaling Quantum Algorithms via Dissipation: Avoiding Barren Plateaus" by Elias Zapusek, Ivan Rojkov, Florentin Reiter Abstract: Variational quantum algorithms (VQAs) have enabled a wide range of applications on near-term quantum devices. However, their scalability is fundamentally limited by barren plateaus, where the probability of encountering large gradients vanishes exponentially with system size. In addition, noise induces barren plateaus, deterministically flattening the cost landscape. Dissipative quantum algorithms that leverage nonunitary dynamics to prepare quantum states via engineered cooling offer a complementary framework with remarkable robustness to noise. We demonstrate that dissipative quantumalgorithms based on non-unital channels can avoid both unitary and noise-induced barren plateaus. Periodically resetting ancillary qubits actively extracts entropy from the system, maintaining gradient magnitudes and enabling scalable optimization. We provide analytic conditions ensuring they remain trainable even in the presence of noise. Numerical simulations confirm our predictions and illustrate scenarios where unitary algorithms fail but dissipative algorithms succeed. Our framework positions dissipative quantum algorithms as a scalable, noise-resilient alternative to traditional VQAs. Link: https://lnkd.in/eeVSVUyP #quantummachinelearning #variationalprinciple #vqa #barrenplateaus