Principles Behind Quantum Computer Feasibility

# How Quantum Computing Works: Quantum computers use qubits instead of classical bits, which can exist in superposition—representing both 0 and 1 simultaneously—allowing them to explore multiple solutions at once. Qubits become entangled, linking their states so that changing one instantly affects others, regardless of distance, which enables exponential computational power as more qubits are added. Operations occur via quantum gates like Hadamard for superposition or CNOT for entanglement, followed by interference to amplify correct answers and cancel incorrect ones before measurement collapses the superposition to a classical result. # Near-Term Applications: Quantum computing targets optimization in supply chains, finance, and logistics within 5 years, alongside breakthroughs in materials science for batteries and quasicrystals. Drug discovery and quantum chemistry benefit from simulations of molecular interactions that classical systems can't handle efficiently, with algorithms improving rapidly. By 2030-2035, fault-tolerant systems could impact aerospace for fuel efficiency and energy for fusion research, while cryptography faces risks from code-breaking but gains post-quantum defenses. # Google's Progress and Timeline: Sundar Pichai stated in late 2025 that Google's quantum efforts are at a tipping point, akin to AI five years prior, predicting practical utility and major breakthroughs in five years through precise simulations of nature. Recent advances like the Quantum Echoes algorithm demonstrate verifiable quantum advantage, advancing real-world uses. Investments position early adopters like Google to lead in fields from drug discovery to climate modelling. Quantum computing relies on principles like superposition and entanglement, using qubits as its core units instead of classical bits. # Key Terms: Qubit: The basic unit of quantum information, unlike a classical bit that is strictly 0 or 1, a qubit can represent both states simultaneously due to quantum properties. Superposition: Allows a qubit to exist in multiple states (like 0 and 1) at once, enabling quantum computers to process many possibilities in parallel, much like overlapping waves. Entanglement: Links qubits so the state of one instantly influences another, even at a distance, creating correlated outcomes that boost computational power exponentially. Quantum Gate: Operations similar to classical logic gates, such as the Hadamard gate for creating superposition or CNOT for entanglement, used to build quantum circuits. Decoherence: The loss of quantum properties when qubits interact with the environment, causing them to behave classically and posing a key challenge for stable computations. Quantum Supremacy: Milestone when a quantum computer solves a problem infeasible for classical supercomputers in reasonable time, as claimed by Google in 2019.

When people say quantum computers “search everything at once,” Grover’s algorithm shows why that’s the wrong picture. What actually happens is far more precise: probability is reshaped. Each Grover iteration is a controlled geometric rotation that suppresses wrong answers and amplifies the correct one through interference. No parallel guessing. No brute force. Just disciplined manipulation of amplitudes until measurement becomes overwhelmingly biased toward the solution. This is why Grover matters beyond textbook search. It teaches the core skill behind many quantum algorithms: how to engineer interference so that information survives measurement instead of collapsing into noise. Once you understand this, ideas like amplitude amplification, oracle design, and optimal iteration counts stop feeling mysterious and start feeling inevitable. If you’re building intuition for quantum computing beyond formulas, this perspective is foundational. For structured, hands-on learning and deeper intuition, explore: 🔗 https://lnkd.in/gwcXHruy Save this if you’re learning quantum computing seriously. #QuantumComputing #Qiskit #GroversSearch

To build powerful quantum computers, we need to correct errors. One promising, hardware-friendly approach is to use 𝘣𝘰𝘴𝘰𝘯𝘪𝘤 𝘤𝘰𝘥𝘦𝘴, which store quantum information in superconducting cavities. These cavities are especially attractive because they can preserve quantum states far longer than even the best superconducting qubits. But to manipulate the quantum state in the cavity, you need to connect it to a ‘helper’ qubit - typically a transmon. Unfortunately, while effective, transmons often introduce new sources of error, including extra noise and unwanted nonlinearities that distort the cavity state. Interestingly, the 𝗳𝗹𝘂𝘅𝗼𝗻𝗶𝘂𝗺 𝗾𝘂𝗯𝗶𝘁 offers a powerful alternative, with several advantages for controlling superconducting cavities: • 𝗠𝗶𝗻𝗶𝗺𝗶𝘀𝗲𝗱 𝗗𝗲𝗰𝗼𝗵𝗲𝗿𝗲𝗻𝗰𝗲: Fluxonium qubits have demonstrated millisecond coherence times, minimising qubit-induced decoherence in the cavity. • 𝗛𝗮𝗺𝗶𝗹𝘁𝗼𝗻𝗶𝗮𝗻 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴: Its rich energy level structure offer significant design flexibility. This allows the qubit-cavity Hamiltonian to be tailored to minimize or eliminate undesirable nonlinearities. • 𝗞𝗲𝗿𝗿-𝗙𝗿𝗲𝗲 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Numerical simulations show that a fluxonium can be designed to achieve a large dispersive shift for fast control, while simultaneously making the self-Kerr nonlinearity vanish. This is a regime that is extremely difficult for a transmon to reach without significant, undesirable qubit-cavity hybridisation.    And there are now experimental results that support this approach. Angela Kou's team coupled a fluxonium qubit to a superconducting cavity, generating Fock states and superpositions with fidelities up to 91%. The main limiting factors were qubit initialisation inefficiency and the modest 12μs lifetime of the cavity in this prototype. Simulations suggest that in higher-coherence systems (like 3D cavities), the fidelity could climb much higher with error rates dropping below 1%. Even more impressive: They show that an external magnetic flux can be used to tune the dispersive shift and self-Kerr nonlinearity independently. So the experiment confirms that there are operating points where the unwanted Kerr term crosses zero while the desired dispersive coupling stays large. In short: Fluxonium qubits offer a practical, tunable path to high-fidelity bosonic control without sacrificing the long lifetimes that make cavity-based quantum memories so attractive in the first place. 📸 Credits: Ke Ni et al. (arXiv:2505.23641) Want more breakdowns and deep dives straight to your inbox? Visit my profile/website to sign up. ☀️

Dear Prof Feynman, Since your 1982 paper “Simulating Physics with Computers,” quantum computing has developed from speculation into experimental reality. Here’s where we stand in June 2025. Your insight that classical computers cannot efficiently simulate quantum systems proved correct - this became the foundation for building quantum computers. Ion trapping techniques developed in the 1980s now control dozens of trapped ions as quantum bits, enabling high accuracy in single quantum operations and extended coherence times. Josephson junctions became artificial atoms: superconducting circuits that manipulate quantum states at millikelvin temperatures. Current superconducting processors include Google’s Willow chip and IBM’s advanced systems. Two-qubit gate accuracies approach 99%, though environmental noise still limits algorithmic applications to dozens of useful qubits working together. Shor’s factoring algorithm works on small numbers but would need millions of error-corrected quantum bits for practical cryptography. Google’s 2019 quantum demonstration solved a sampling problem faster than classical computers, though the practical advantage is close to nil. Scientists have built logical quantum bits that actually last longer and make fewer errors than the physical quantum bits they’re made from. However, fault-tolerant computation requires significant overhead, necessitating many physical quantum bits per logical quantum bit. IBM plans to develop 200-logical-qubit systems by 2029, utilizing advanced error correction codes. Your original challenge persists. Quantum many-body systems remain exponentially hard to simulate classically, yet building quantum simulators requires controlling thousands of quantum components with extraordinary precision.

PsiQuantum Achieves Breakthrough in Mass-Producing Light-Powered Quantum Chips American quantum computing startup PsiQuantum has announced a major breakthrough in manufacturing scalable photonic quantum chips, marking a significant step toward making practical quantum computing a reality. The company, which emerged from stealth mode in 2021, has been working on a light-powered (photonic) quantum computing approach, which was previously considered impractical due to hardware limitations. Why Photonic Quantum Computing? • Photonic quantum computers encode data in individual particles of light (photons), rather than in superconducting circuits like many other quantum systems. • This approach has key advantages: • Low noise compared to superconducting qubits. • High-speed operation due to the natural speed of light. • Seamless integration with fiber-optic networks, which could make quantum internet feasible. • However, the challenge has always been scaling up, as photons are difficult to control, detect, and stabilize in large-scale computations. PsiQuantum’s Breakthrough • In a paper published in Nature, the company unveiled a manufacturing process that enables large-scale production of photonic quantum chips. • The new hardware design solves key engineering problems, making it possible to reliably manipulate and measure photons at scale. • Unlike previous photonic quantum systems, which struggled with extreme hardware demands, PsiQuantum’s solution reduces errors and improves stability in complex computations. Implications for the Future of Quantum Computing • Scalability Achieved – This breakthrough could allow for mass production of quantum chips, removing a key bottleneck in commercial quantum computing development. • Quantum Networking Potential – With natural fiber-optic compatibility, photonic quantum computers could lead to highly secure quantum communications networks. • New Industrial Applications – The technology may soon be applied to optimization problems, cryptography, and materials science, revolutionizing industries that require complex simulations. The Bigger Picture PsiQuantum’s ability to mass-produce photonic quantum chips puts light-powered quantum computing in direct competition with other approaches, such as superconducting and trapped-ion quantum systems. If successful, it could make quantum computing more accessible, scalable, and commercially viable—a leap forward in the race to achieve practical quantum supremacy.

The world is not classical, and if we want to simulate it, we must build machines that speak its native language: quantum mechanics. In 1981, Richard Feynman delivered a keynote that effectively launched the field of quantum computing. His core argument was as elegant as it was challenging. He observed that classical computers face an exponential wall when trying to simulate quantum systems. To model even a tiny fragment of the subatomic world, a classical machine requires an impossible amount of memory and time. Feynman’s proposal was radical for its time. Instead of fighting the complexity of quantum mechanics with classical bits, we should use those very complexities—superposition and interference—as computational resources. He envisioned a new class of universal simulators that don’t just approximate nature but operate on the same fundamental principles. Decades later, we are moving from his theoretical vision to physical hardware. Whether we are discussing silicon-vacancy centers in diamond or superconducting qubits, we are finally building the "quantum mechanical" machines Feynman called for. The goal is no longer just to calculate, but to provide a window into how the universe actually functions at its most granular level. As we celebrate World Quantum Day, we are reminded that the most significant breakthroughs often start with a simple shift in perspective. How do you think our approach to problem-solving changes when we finally have the tools to simulate nature without compromise? #WorldQuantumDay #QuantumComputing #Physics #DeepTech #Computation #Feynman #Innovation

Quantum computing is evolving at an incredible pace, and among the many hardware platforms being explored, trapped-ion technology stands out as one of the most promising candidates for building scalable, high-fidelity quantum computers. But how exactly do these systems work? For the past three years, we’ve collaborated with Quantinuum on the “Quantum + Chips” summer school series, dedicating time to teaching undergraduates the fundamental principles of trapped-ion quantum computing. As promised, I’ve finally put together the first video in a series that breaks down the physics and engineering behind these powerful quantum systems. https://lnkd.in/gwZYNuNP In this video, we explore the fundamental physics behind trapped ions and how their internal states are leveraged for complex quantum operations. We explain how ions function as qubits, with their electronic and vibrational states forming the foundation of quantum computation. You’ll learn the differences between optical and hyperfine qubits, and why phonon modes act as a "quantum bus," enabling qubits to interact. We also break down key concepts like sideband cooling, qubit initialization, and quantum state readout, all of which are essential for high-precision quantum operations. With trapped-ion systems now achieving quantum gate fidelities above 99%, they meet the stringent requirements for practical quantum computing. While scaling up to thousands of qubits remains a challenge, the progress so far suggests that this technology has the potential to be a dominant platform in the future of quantum information processing.

Don’t sleep on quantum computing! While AI dominates the headlines, quantum computing is progressing faster than most realize. The field is now moving from physics labs into commercial pilots, with fault-tolerant systems expected before 2030. Classical computers process information in bits, zero or one. Quantum computers use qubits. A qubit can exist in a superposition of both zero and one, and when qubits are entangled their states are linked regardless of distance. This creates an exponentially large computational space. A system of 100 qubits can represent 2¹⁰⁰ states simultaneously, more than the number of atoms in the universe. The main barriers have been decoherence, noise, and error rates. In 2025, groups such as Google and Quantinuum demonstrated quantum error correction where a logical qubit had lower error than the physical qubits it was built from. Gate fidelities above 99.9% and millisecond coherence times are now routine. Applications are no longer in the lab. HSBC reported a 34% improvement in bond trading execution with IBM. DHL reduced delivery times by 20% using quantum route optimization. Pfizer and Moderna are running quantum molecular simulations for drug discovery. For telecommunications, the challenge is urgent. Current encryption will fail against large-scale quantum computers, while the opportunities lies in quantum secure networks and the future quantum internet. https://lnkd.in/gidSwn4E

#IBM's quantum computing systems operate by leveraging the principles of quantum mechanics to process information in ways that classical computers cannot. Here's a step-by-step overview of how IBM's quantum computers work: --- 1. #SuperconductingQubits IBM's quantum computers use superconducting qubits, which are tiny circuits made from superconducting materials that operate at near absolute zero temperatures. These qubits exhibit quantum properties like superposition and entanglement. --- 2. #QuantumStates Superposition: Qubits can exist in multiple states (0 and 1) simultaneously, enabling parallel computations. Entanglement: Qubits can become entangled, meaning their states are interconnected, allowing for complex correlations that enhance computational power. --- 3. #QuantumCircuits Programming with Qiskit: IBM provides the Qiskit framework, allowing users to create quantum circuits, which are sequences of quantum operations (gates) applied to qubits. Quantum Gates: Gates manipulate the quantum states of qubits, similar to how logic gates operate in classical computing. Examples include Hadamard, Pauli, and CNOT gates. --- 4. #CryogenicEnvironment Qubits are extremely sensitive to external disturbances, such as heat, noise, and electromagnetic radiation. To maintain their quantum properties: IBM quantum computers are housed in dilution refrigerators that cool them to millikelvin temperatures, close to absolute zero. This minimizes decoherence (loss of quantum information) and allows the qubits to operate reliably. --- 5. #Control and Measurement #ControlSignals: Microwave pulses and magnetic fields are used to control the qubits, applying specific operations (quantum gates) as defined in the quantum circuit. #Measurement: At the end of a computation, qubits are measured. This collapses their quantum state into a classical state (0 or 1), providing the result of the computation. --- 6. #ErrorCorrection Quantum computers are prone to errors due to noise and decoherence. #IBM uses quantum error correction codes and advanced techniques to detect and mitigate errors, ensuring reliable results. --- 7. #Cloud-Based Access IBM’s quantum computers are accessible through the IBM Quantum Platform on the cloud. Researchers, developers, and businesses can use the platform to run quantum experiments, develop algorithms, and simulate quantum systems. --- 8. #Applications Optimization problems Machine learning and artificial intelligence Cryptography and secure communications Molecular simulations for material science and pharmaceuticals --- #SummaryWorkflow 1. 🔹Create a quantum circuit using Qiskit. 2. 🔹Upload the circuit to IBM’s quantum computer via the cloud. 3. 🔹The system executes the circuit on its quantum processor. 4. 🔹The results are measured, processed, and sent back to the user. 🔹IBM continues to improve its quantum computing hardware and software #QuantumComputing #IBM

⚛️ A Gateway to Quantum Computing for Industrial Engineering 📑 Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engineering (IE) and operations research (OR), this shift introduces both unprecedented opportunities and substantial challenges. The learning curve is high, and to help researchers navigate the emerging field of quantum operations research, we provide a road map of the current field of quantum operations research. We introduce the foundational principles of quantum computing, outline the current hardware and software landscape, and survey major algorithmic advances relevant to IE/OR, including quantum approaches to linear algebra, optimization, machine learning, and stochastic simulation. We then highlight applied research directions, including the importance of problem domains for driving long-term value of quantum computers and how existing classical OR models can be reformulated for quantum hardware. Recognizing the steep learning curve, we propose pathways for IE/OR researchers to develop technical fluency and engage in this interdisciplinary domain. By bridging theory with application, and emphasizing the interplay between hardware and research development, we argue that industrial engineers are uniquely positioned to shape the trajectory of quantum computing for practical problem-solving. Ultimately, we aim to lower the barrier to entry into QOR, motivate new collaborations, and chart future directions where quantum technologies may deliver tangible impact for industry and academia ℹ️ E. Tucker & M. Mohammadisiahroudi - 2025