FAQ

Q&A on Quantum Mechanics & Computing

What is a Qubit? How do we use it for calculations?

A qubit (quantum bit) is the fundamental unit of quantum information—the quantum equivalent of a classical bit. Unlike a classical bit that must be either 0 or 1, a qubit can exist in a superposition of both states simultaneously. Qubits are implemented in engineered systems that isolate and control quantum properties:
• Trapped ion qubits: We trap real ions (charged atoms) and use their energy levels as |0⟩ and |1⟩ states.
• Superconducting qubits: We fabricate superconducting circuits where current flow directions represent the qubit states
• Photonic qubits: We generate photons and use their polarization states
• Spin qubits: We isolate individual electrons and use their spin properties The key is that we engineer these systems to behave as controllable two-level quantum systems—that's what makes them "qubits."
We manipulate them using carefully controlled electromagnetic fields, laser pulses, or microwave radiation to:
1. Prepare them in desired superposition states 2. Rotate their quantum states through quantum gates 3. Entangle multiple qubits together 4. Measure their final states to read out the computation results.

Can quantum solve all mathematical problems?

Many target problems are NP-hard, meaning the number of possibilities grows exponentially. Quantum doesn’t solve NP-hard problems, but hybrid quantum–classical methods can often find better solutions faster on practical instances and re-optimize in real time as conditions change. Quantum circuits explore many possibilities in parallel and use interference to amplify good candidates, while the classical side evaluates candidates against the objective, enforces off-circuit constraints, and refines the final answer.

What is superposition? Is a particle really in two places at once? What does that have to do with quantum computing?

Superposition is a fundamental principle of quantum mechanics that allows a quantum system, such as a qubit, to exist in a single, non-classical state defined by a blend of multiple basis states' probabilities. This blend determines the probability of observing a specific state when measured (for example, a 50-50% probability). In practice, we often intentionally prepare qubits in specific states, like equal parts 0 and 1 (a 50-50% probability superposition), by manipulating their energy levels, assigning '0' to the lower and '1' to the higher energy state. Mathematically, a qubit's superposition is expressed as α|0⟩ + β|1⟩, where the amplitudes α and β are complex numbers representing probability amplitudes. These complex amplitudes encode both the probability magnitude and the phase, which is crucial for quantum phenomena like interference, with the normalization rule |α|² + |β|² = 1. The physical reality of superposition is verified in the lab: for example, atoms (like chlorine) can be driven into mid-energy states by carefully applied electromagnetic waves, occupying both possible levels until measured. In quantum computing, these prepared and balanced states let us encode and process many solutions at the same time, vastly exceeding classical computational capability.

How can superposition help with large calculations if it’s only 0 or 1?

Multiple qubits let us work with many binary sequences at once, which is “quantum parallelism”. With n qubits we prepare a state that includes 2ⁿ bitstrings, (e.g., with 6 qubits we cover 64 sequences: 000000 … 111111). Gates act on all of those sequences together, nudging their amplitudes and phases so that undesirable sequences cancel and promising sequences are reinforced. Once we measure, we see one sequence. We are actually shaping a whole cloud of possibilities, so the right patterns are the ones that pop out most often.

If measurement gives only one answer—and devices are noisy—how can we trust the result?

We sample the circuit many times (“shots,” e.g., 500–1–000) and look at the distribution of outcomes. We also use calibration and error-mitigation techniques. Depending on the problem, we’ll pick the most frequent bitstring, the lowest “energy” solution, or the best-scoring candidate under your objective. As hardware improves, fewer shots and reruns are needed. This is referred to as “most frequent bitstring:” After many runs (“shots”) of a quantum circuit, the result that occurs most often (the most observed sequence of bits) is often interpreted as the “optimal” answer, especially for combinatorial optimization.

What is entanglement? Does an action on one particle influence the other?

Entangled qubits are prepared so their measurement outcomes are correlated, even when separated. Measuring one immediately tells you what you'll find on the other when measured in a matching basis. This correlation exists because of how they were initially prepared together, not because measuring one particle somehow signals or changes the other. The "spooky action at a distance" Einstein worried about is really just revealing these initial correlations, not transmitting influence. Entanglement is useful because it encodes relationships between variables directly into the quantum state. Environmental noise, direct measurement, or decoherence destroys entanglement.

What is interference?

Interference is how quantum amplitudes add or cancel, like we see when water waves collide. By applying gates that adjust the phases of amplitudes, we make “bad” answers cancel each other and “good” answers reinforce. We guide the system according to our problem’s constraints and rules, specific ranges, interdependencies etc. After interference has concentrated probability on the “good” region of the search space, a measurement is more likely to return a high-quality solution.

Can we use quantum computers now to solve real-world problems?

Current quantum computers are still in the "noisy intermediate-scale quantum" (NISQ) era. They have a limited number of qubits, are highly sensitive to noise and environmental factors, and can only run short computations. For these reasons, a purely quantum approach for many problems would be ineffective, as the quantum advantage would be lost to noise before the algorithm could finish. Instead, most practical applications today use a hybrid quantum-classical approach, which leverages the strengths of both quantum and classical computers to solve a problem. QSphera utilizes a smart compression mechanism enabling quantum optimization for certain purposes.

How can I use a quantum computer? Can I buy one?

The cost of a fully-operational, industrial-grade quantum computer with error correction can range from $10-50 million, making a direct purchase impractical for most people. However, the primary way to access a quantum computer is through cloud services. Major technology companies and dedicated quantum startups provide access to their quantum hardware and simulators via the cloud. This allows developers, researchers, and students to run experiments and build applications using powerful quantum systems without needing to own or maintain the expensive, specialized hardware.

How does QSphera deal with the limitation of stable qubits?

QSphera addresses the limitation of stable qubits using a unique Compressed Indices Method. This proprietary mathematical approach, with a process streamlined by AI, allows the platform to represent a vast number of classical variables using a limited number of qubits. By pre-processing data and creating these compressed indices, the system can efficiently run complex calculations on current quantum hardware. It then uses a smart algorithm and AI to post-process the results, refining them to provide a complete and accurate solution.

What is quantum-for-quantum™?

Quantum-for-quantum is QSphera's approach to using pure quantum methods to improve other quantum processes, rather than just using a hybrid approach. These unique methods, which are mostly in development, are proprietary to QSphera. For example, their Q-DIG module is a prime example of a quantum-for-quantum method that is already in a mature state.

What is Q-DIG™?

Q-DIG™ (Quantum Dynamic Imputation Generator) is a QSphera proprietary quantum optimization engine designed to intelligently and coherently fill missing or distorted data points. Many real-world datasets have crucial missing parameters that traditional classical imputation methods cannot handle accurately or require vast amounts of learning time. Unlike classical methods, Q-DIG utilizes the system's quantum state to test a full range of possibilities simultaneously, precisely locating the most globally consistent and error-free value for the unknown parameter. It can also be provided as a standalone classical service for clients who only need a robust data imputation solution.

What is optimization used for?

Choosing the best feasible plan under goals and constraints when there are too many possibilities to check one by one. QSphera examples:

• Agriculture: Plan irrigation/fertilization per field zone and day; re-optimize after heatwaves or pest events; route machinery efficiently.

• Energy & grids: Dispatch storage, balance loads, and choose feeder switches for reliability and cost.

• Logistics & manufacturing: Build delivery routes and time windows; sequence machines; generate shift rosters under labor rules.

• Finance (where relevant): Select portfolios under risk/limit constraints with fast re-optimization as markets move.

How are the amplitude and phase of a qubit calculated?

The amplitude and phase of a quantum state are calculated from the complex numbers that define the state. A qubit's state is described by two complex amplitudes, α and β, which must obey the normalization rule ∣α∣² +∣β∣² =1. α and β, which must obey the normalization rule ∣α∣2+∣β∣2=1. Calculating the Amplitude The amplitude is the magnitude of the complex number. It's the length of the vector in the complex plane and is found using the Pythagorean theorem. For an amplitude α=a+bi, its magnitude is calculated as: |α| = √(a² + b²) The probability of measuring the qubit in that state is the square of this magnitude, ∣α∣². Calculating the Phase The phase is the angle of the complex number in the complex plane. For a complex number α = a + bi, the phase θ is calculated using: θ = arctan(b/a) It's important to use a four-quadrant arctangent function (like atan2 in programming) to get the correct angle for all quadrants. The complex amplitude can then be written in polar form as α = |α|e^(iθ), where |α| is the magnitude and θ is the phase.