While various studies have hinted at the existence of dark matter, its nature, composition and underlying physics remain poorly understood.
Quantum state tomography plays a fundamental role in characterizing and evaluating the quality of quantum states produced by quantum devices. It serves as a crucial element in the advancement of quantum hardware and software, regardless of the underlying physical implementation and potential applications1,2,3. However, reconstructing the full quantum state becomes prohibitively expensive for large-scale quantum systems that exhibit potential quantum advantages4,5, as the number of measurements required increases exponentially with system size.
Recent protocols try to solve this challenge through two main steps: efficient parameterization of quantum states and utilization of carefully designed measurement schemes and classical data postprocessing algorithms. For one-dimensional (1D) systems with area law entanglement, the matrix product state (MPS)6,7,8,9,10,11,12 provides a compressed representation. It requires only a polynomial number of parameters that can be determined from local or global measurement results. Two iterative algorithms using local measurements, singular value thresholding (SVT)13 and maximum likelihood (ML)14, have been demonstrated in trapped-ion quantum simulators with up to 14 qubits15. However, SVT is limited to pure states and thus impractical for noisy intermediate-scale quantum (NISQ) systems. Meanwhile, although ML can handle mixed states represented as matrix product operators (MPOs)16,17, it suffers from inefficient classical data postprocessing.
The authors explore the digital-analog quantum computing paradigm, which combines fast single-qubit gates with the natural dynamics of quantum devices. They find the digital-analog paradigm more robust against certain experimental imperfections than the standard fully-digital one and successfully apply error mitigation techniques to this approach.
China’s efforts to scale up the manufacture of superconducting quantum computers have gathered momentum with the launch of the country’s independently developed third-generation Origin Wukong, said industry experts on Monday.
The latest quantum computer, which is powered by Wukong, a 72-qubit indigenous superconducting quantum chip, has become the most advanced programmable and deliverable superconducting quantum computer currently available in China.
The chip was developed by Origin Quantum, a Hefei, Anhui province-based quantum chip startup. The company has already delivered its first and second generations of superconducting quantum computers to the Chinese market.
Quantum AI, the fusion of quantum computing and artificial intelligence, is poised to revolutionize industries from finance to healthcare.
Researchers at KIT’s Physikalisches Institut have developed a method to precisely control diamond tin-vacancy qubits.
Researchers from Tohoku University and the Massachusetts Institute of Technology (MIT) have unveiled a new AI tool for high-quality optical spectra with the same accuracy as quantum simulations, but working a million times faster, potentially accelerating the development of photovoltaic and quantum materials.
Scientists are finding ways to use quantum effects to create groundbreaking thermal devices that can help cool electronic systems. The quantum thermal transistor is one of the most exciting innovations in this field. While the current works surrounding this device are still theoretical, recent advancements in the fabrication of qubits using quantum dots and superconducting circuits have created a growing sense of optimism.
Discover how the Quantum Zeno Effect can freeze quantum systems in time. Learn its applications in quantum computing and biology. Explore with us!
Despite the promising findings, the study acknowledges several limitations of quantum computing. One of the primary challenges is hardware noise, which can reduce the accuracy of quantum computations. Although error correction methods are improving, quantum computing has not yet reached the level of fault tolerance needed for widespread commercial use. Additionally, the study notes that while quantum computing has shown promise in PBPK/PD modeling and site selection, further research is needed to fully realize its potential in these areas.
Looking ahead, the study suggests several future directions for research. One of the key areas for improvement is the integration of quantum algorithms with existing clinical trial infrastructure. This will require collaboration between researchers, pharmaceutical companies and regulators to ensure that quantum computing can be effectively applied in real-world clinical settings. Additionally, the study calls for more work on developing quantum algorithms that can handle the inherent variability in biological data, particularly in genomics and personalized medicine.
The research was conducted by a team from several prominent institutions. Hakan Doga, Aritra Bose, and Laxmi Parida are from IBM Research and IBM Quantum. M. Emre Sahin is affiliated with The Hartree Centre, STFC, while Joao Bettencourt-Silva is based at IBM Research, Dublin, Ireland. Anh Pham, Eunyoung Kim, Anh Pham, Eunyoung Kim and Alan Andress are from Deloitte Consulting LLP. Sudhir Saxena and Radwa Soliman are from GNQ Insilico Inc. Jan Lukas Robertus is affiliated with Imperial College London and Royal Brompton and Harefield Hospitals and Hideaki Kawaguchi is from Keio University. Finally, Daniel Blankenberg is from the Lerner Research Institute, Cleveland Clinic.
